"Unsupervised Source Separation with Learned Regularization"Carloni Gertosio, RémiBlind source separation (BSS) algorithms are unsupervised methods that allow for physically meaningful decompositions of multispectral data. Being ill-posed inverse problems, BSS algorithms must rely on efficient regularization schemes to better distinguish between the sources and yield interpretable solutions. For that purpose, we propose a semi-blind source separation approach coined sGMCA in which we combine a projected alternate least-squares algorithm with a learning-based regularization scheme. |
| http://univie.ac.at/projektservice-mathematik/e/talks/Carloni Gertosio_2021-06_ICCHA2021.pdf |
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